Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7f9974d16cc0>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f9974c69a90>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.8.0
Default GPU Device: /device:GPU:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')

    learn_rate = tf.placeholder(tf.float32, name='learn_rate')
    
    return inputs_real, inputs_z , learn_rate

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('generator', reuse= not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 2*2*512)
        
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 2, 2, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
      
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
 
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
    
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    #alpha = 0.2
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    n_samples, width, height, channels = data_shape
    input_real, input_z, learn_rate = model_inputs(width, height, channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    steps = 0
    show_every =100
    print_every = 10
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                batch_images *= 2
                # TODO: Train Model
                steps += 1
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})

                if steps % show_every == 0:
                    n_images = 16
                    show_generator_output(sess, n_images, input_z, channels, data_image_mode)

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 32
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 5

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

Epoch 0/5... Discriminator Loss: 0.7046... Generator Loss: 0.9460
Epoch 0/5... Discriminator Loss: 0.3665... Generator Loss: 1.5449
Epoch 0/5... Discriminator Loss: 0.3662... Generator Loss: 1.8970
Epoch 0/5... Discriminator Loss: 0.5571... Generator Loss: 3.7271
Epoch 0/5... Discriminator Loss: 0.2926... Generator Loss: 1.7817
Epoch 0/5... Discriminator Loss: 0.7351... Generator Loss: 4.8604
Epoch 0/5... Discriminator Loss: 0.6960... Generator Loss: 1.3939
Epoch 0/5... Discriminator Loss: 1.0830... Generator Loss: 0.5221
Epoch 0/5... Discriminator Loss: 0.3240... Generator Loss: 2.1808

Epoch 0/5... Discriminator Loss: 0.3969... Generator Loss: 1.8689
Epoch 0/5... Discriminator Loss: 0.4111... Generator Loss: 1.8683
Epoch 0/5... Discriminator Loss: 0.2772... Generator Loss: 3.9101
Epoch 0/5... Discriminator Loss: 0.2767... Generator Loss: 2.6979
Epoch 0/5... Discriminator Loss: 0.1354... Generator Loss: 2.7041
Epoch 0/5... Discriminator Loss: 0.3557... Generator Loss: 1.9391
Epoch 0/5... Discriminator Loss: 0.1348... Generator Loss: 2.9576
Epoch 0/5... Discriminator Loss: 0.1856... Generator Loss: 2.7871
Epoch 0/5... Discriminator Loss: 0.2727... Generator Loss: 2.4025
Epoch 0/5... Discriminator Loss: 0.1263... Generator Loss: 2.8423

Epoch 0/5... Discriminator Loss: 0.1853... Generator Loss: 2.3881
Epoch 0/5... Discriminator Loss: 0.1142... Generator Loss: 3.1376
Epoch 0/5... Discriminator Loss: 0.1299... Generator Loss: 2.9582
Epoch 0/5... Discriminator Loss: 0.1156... Generator Loss: 3.0997
Epoch 0/5... Discriminator Loss: 0.1214... Generator Loss: 3.0090
Epoch 0/5... Discriminator Loss: 0.1162... Generator Loss: 3.2797
Epoch 0/5... Discriminator Loss: 0.2371... Generator Loss: 2.2430
Epoch 0/5... Discriminator Loss: 0.1595... Generator Loss: 3.0597
Epoch 0/5... Discriminator Loss: 0.0991... Generator Loss: 3.4237
Epoch 0/5... Discriminator Loss: 0.0954... Generator Loss: 3.0576

Epoch 0/5... Discriminator Loss: 0.1064... Generator Loss: 2.9826
Epoch 0/5... Discriminator Loss: 0.1192... Generator Loss: 4.1447
Epoch 0/5... Discriminator Loss: 0.1214... Generator Loss: 2.7600
Epoch 0/5... Discriminator Loss: 0.2338... Generator Loss: 1.9646
Epoch 0/5... Discriminator Loss: 0.1860... Generator Loss: 2.2374
Epoch 0/5... Discriminator Loss: 0.2041... Generator Loss: 3.0807
Epoch 0/5... Discriminator Loss: 0.1031... Generator Loss: 3.7326
Epoch 0/5... Discriminator Loss: 0.1144... Generator Loss: 3.4287
Epoch 0/5... Discriminator Loss: 0.5077... Generator Loss: 6.1645
Epoch 0/5... Discriminator Loss: 0.3248... Generator Loss: 1.6820

Epoch 0/5... Discriminator Loss: 0.2273... Generator Loss: 2.2550
Epoch 0/5... Discriminator Loss: 0.1808... Generator Loss: 2.9944
Epoch 0/5... Discriminator Loss: 0.1755... Generator Loss: 2.7574
Epoch 0/5... Discriminator Loss: 0.3204... Generator Loss: 1.7417
Epoch 0/5... Discriminator Loss: 0.1036... Generator Loss: 3.2904
Epoch 0/5... Discriminator Loss: 0.1753... Generator Loss: 2.8376
Epoch 0/5... Discriminator Loss: 0.1025... Generator Loss: 3.7442
Epoch 0/5... Discriminator Loss: 0.2389... Generator Loss: 3.4421
Epoch 0/5... Discriminator Loss: 0.1292... Generator Loss: 2.9033
Epoch 0/5... Discriminator Loss: 0.1762... Generator Loss: 3.1299

Epoch 0/5... Discriminator Loss: 0.0684... Generator Loss: 3.6672
Epoch 0/5... Discriminator Loss: 0.1709... Generator Loss: 3.0624
Epoch 0/5... Discriminator Loss: 0.1962... Generator Loss: 2.7465
Epoch 0/5... Discriminator Loss: 0.8699... Generator Loss: 6.0987
Epoch 0/5... Discriminator Loss: 0.0947... Generator Loss: 3.4393
Epoch 0/5... Discriminator Loss: 0.3279... Generator Loss: 1.7767
Epoch 0/5... Discriminator Loss: 0.3502... Generator Loss: 1.8709
Epoch 0/5... Discriminator Loss: 0.3752... Generator Loss: 1.5232
Epoch 0/5... Discriminator Loss: 0.9975... Generator Loss: 6.5827
Epoch 0/5... Discriminator Loss: 0.4589... Generator Loss: 1.4296

Epoch 0/5... Discriminator Loss: 0.2985... Generator Loss: 3.0909
Epoch 0/5... Discriminator Loss: 0.3076... Generator Loss: 2.3576
Epoch 0/5... Discriminator Loss: 0.5978... Generator Loss: 4.1385
Epoch 0/5... Discriminator Loss: 0.2797... Generator Loss: 2.1418
Epoch 0/5... Discriminator Loss: 0.3843... Generator Loss: 1.6898
Epoch 0/5... Discriminator Loss: 0.1778... Generator Loss: 2.5731
Epoch 0/5... Discriminator Loss: 0.2627... Generator Loss: 2.6141
Epoch 0/5... Discriminator Loss: 0.2386... Generator Loss: 2.8340
Epoch 0/5... Discriminator Loss: 0.2239... Generator Loss: 3.5406
Epoch 0/5... Discriminator Loss: 0.0998... Generator Loss: 3.1257

Epoch 0/5... Discriminator Loss: 0.4699... Generator Loss: 1.6747
Epoch 0/5... Discriminator Loss: 0.3557... Generator Loss: 2.5881
Epoch 0/5... Discriminator Loss: 0.2571... Generator Loss: 2.9606
Epoch 0/5... Discriminator Loss: 0.2557... Generator Loss: 3.5478
Epoch 0/5... Discriminator Loss: 0.3669... Generator Loss: 2.0864
Epoch 0/5... Discriminator Loss: 0.5413... Generator Loss: 1.3019
Epoch 0/5... Discriminator Loss: 0.3906... Generator Loss: 1.8421
Epoch 0/5... Discriminator Loss: 0.3545... Generator Loss: 3.0980
Epoch 0/5... Discriminator Loss: 0.2849... Generator Loss: 2.6190
Epoch 0/5... Discriminator Loss: 0.3744... Generator Loss: 2.0621

Epoch 0/5... Discriminator Loss: 0.4129... Generator Loss: 2.2154
Epoch 0/5... Discriminator Loss: 0.4230... Generator Loss: 1.5937
Epoch 0/5... Discriminator Loss: 0.3754... Generator Loss: 2.6509
Epoch 0/5... Discriminator Loss: 0.4711... Generator Loss: 3.4478
Epoch 0/5... Discriminator Loss: 0.4794... Generator Loss: 1.5175
Epoch 0/5... Discriminator Loss: 0.7110... Generator Loss: 1.0057
Epoch 0/5... Discriminator Loss: 0.3941... Generator Loss: 2.1796
Epoch 0/5... Discriminator Loss: 0.3522... Generator Loss: 2.7522
Epoch 0/5... Discriminator Loss: 0.4204... Generator Loss: 2.0111
Epoch 0/5... Discriminator Loss: 0.4659... Generator Loss: 1.9522

Epoch 0/5... Discriminator Loss: 0.2768... Generator Loss: 2.2447
Epoch 0/5... Discriminator Loss: 0.4177... Generator Loss: 2.4093
Epoch 0/5... Discriminator Loss: 0.4954... Generator Loss: 1.8363
Epoch 0/5... Discriminator Loss: 0.5991... Generator Loss: 1.1311
Epoch 0/5... Discriminator Loss: 0.2844... Generator Loss: 2.7491
Epoch 0/5... Discriminator Loss: 0.6227... Generator Loss: 1.7591
Epoch 0/5... Discriminator Loss: 0.3755... Generator Loss: 2.5221
Epoch 0/5... Discriminator Loss: 0.7908... Generator Loss: 0.9832
Epoch 0/5... Discriminator Loss: 0.3998... Generator Loss: 1.8420
Epoch 0/5... Discriminator Loss: 0.6887... Generator Loss: 1.3163

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 5

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Epoch 0/5... Discriminator Loss: 1.8813... Generator Loss: 0.3869
Epoch 0/5... Discriminator Loss: 0.8844... Generator Loss: 1.0501
Epoch 0/5... Discriminator Loss: 0.5608... Generator Loss: 1.4488
Epoch 0/5... Discriminator Loss: 0.3898... Generator Loss: 1.7532
Epoch 0/5... Discriminator Loss: 0.3181... Generator Loss: 1.9442
Epoch 0/5... Discriminator Loss: 0.2443... Generator Loss: 2.1909
Epoch 0/5... Discriminator Loss: 0.2588... Generator Loss: 2.2054
Epoch 0/5... Discriminator Loss: 0.2620... Generator Loss: 2.2299
Epoch 0/5... Discriminator Loss: 0.2825... Generator Loss: 2.0621

Epoch 0/5... Discriminator Loss: 0.1918... Generator Loss: 2.1659
Epoch 0/5... Discriminator Loss: 0.2220... Generator Loss: 2.7804
Epoch 0/5... Discriminator Loss: 0.0870... Generator Loss: 3.8087
Epoch 0/5... Discriminator Loss: 0.2449... Generator Loss: 4.1270
Epoch 0/5... Discriminator Loss: 0.2330... Generator Loss: 5.7021
Epoch 0/5... Discriminator Loss: 0.2137... Generator Loss: 2.1514
Epoch 0/5... Discriminator Loss: 0.3058... Generator Loss: 2.1009
Epoch 0/5... Discriminator Loss: 0.2778... Generator Loss: 6.3896
Epoch 0/5... Discriminator Loss: 0.1300... Generator Loss: 3.2060
Epoch 0/5... Discriminator Loss: 0.3238... Generator Loss: 2.0640

Epoch 0/5... Discriminator Loss: 0.2729... Generator Loss: 3.2184
Epoch 0/5... Discriminator Loss: 0.2388... Generator Loss: 4.7576
Epoch 0/5... Discriminator Loss: 0.3957... Generator Loss: 2.9482
Epoch 0/5... Discriminator Loss: 0.5800... Generator Loss: 1.3964
Epoch 0/5... Discriminator Loss: 0.4301... Generator Loss: 1.2214
Epoch 0/5... Discriminator Loss: 0.3720... Generator Loss: 2.1777
Epoch 0/5... Discriminator Loss: 0.4392... Generator Loss: 1.5529
Epoch 0/5... Discriminator Loss: 0.5133... Generator Loss: 2.5968
Epoch 0/5... Discriminator Loss: 0.3341... Generator Loss: 3.8950
Epoch 0/5... Discriminator Loss: 0.2431... Generator Loss: 1.9408

Epoch 0/5... Discriminator Loss: 0.4771... Generator Loss: 1.6744
Epoch 0/5... Discriminator Loss: 0.3095... Generator Loss: 3.1099
Epoch 0/5... Discriminator Loss: 0.8920... Generator Loss: 3.2310
Epoch 0/5... Discriminator Loss: 0.5063... Generator Loss: 1.7913
Epoch 0/5... Discriminator Loss: 0.4510... Generator Loss: 1.7779
Epoch 0/5... Discriminator Loss: 0.7041... Generator Loss: 3.2947
Epoch 0/5... Discriminator Loss: 0.5652... Generator Loss: 1.7102
Epoch 0/5... Discriminator Loss: 0.3870... Generator Loss: 2.0936
Epoch 0/5... Discriminator Loss: 0.4909... Generator Loss: 2.8955
Epoch 0/5... Discriminator Loss: 0.5878... Generator Loss: 1.8507

Epoch 0/5... Discriminator Loss: 0.6647... Generator Loss: 0.9347
Epoch 0/5... Discriminator Loss: 0.9719... Generator Loss: 0.8012
Epoch 0/5... Discriminator Loss: 1.0601... Generator Loss: 2.2601
Epoch 0/5... Discriminator Loss: 0.9457... Generator Loss: 2.4462
Epoch 0/5... Discriminator Loss: 0.8233... Generator Loss: 1.5532
Epoch 0/5... Discriminator Loss: 1.0975... Generator Loss: 0.6200
Epoch 0/5... Discriminator Loss: 0.5748... Generator Loss: 2.0945
Epoch 0/5... Discriminator Loss: 0.6867... Generator Loss: 1.8230
Epoch 0/5... Discriminator Loss: 1.0525... Generator Loss: 2.8599
Epoch 0/5... Discriminator Loss: 0.8460... Generator Loss: 1.6159

Epoch 0/5... Discriminator Loss: 0.6097... Generator Loss: 1.5992
Epoch 0/5... Discriminator Loss: 0.6603... Generator Loss: 1.1812
Epoch 0/5... Discriminator Loss: 0.6592... Generator Loss: 1.3772
Epoch 0/5... Discriminator Loss: 0.7252... Generator Loss: 1.3616
Epoch 0/5... Discriminator Loss: 0.7479... Generator Loss: 1.1599
Epoch 0/5... Discriminator Loss: 0.5563... Generator Loss: 2.0425
Epoch 0/5... Discriminator Loss: 0.6845... Generator Loss: 1.5256
Epoch 0/5... Discriminator Loss: 0.8916... Generator Loss: 1.2853
Epoch 0/5... Discriminator Loss: 0.6638... Generator Loss: 1.2339
Epoch 0/5... Discriminator Loss: 0.6102... Generator Loss: 1.4245

Epoch 0/5... Discriminator Loss: 0.6759... Generator Loss: 1.1802
Epoch 0/5... Discriminator Loss: 0.6767... Generator Loss: 1.6524
Epoch 0/5... Discriminator Loss: 0.7976... Generator Loss: 1.2049
Epoch 0/5... Discriminator Loss: 0.7690... Generator Loss: 1.1035
Epoch 0/5... Discriminator Loss: 0.9203... Generator Loss: 1.4923
Epoch 0/5... Discriminator Loss: 0.7993... Generator Loss: 1.3747
Epoch 0/5... Discriminator Loss: 0.8126... Generator Loss: 1.4008
Epoch 0/5... Discriminator Loss: 0.9334... Generator Loss: 0.8942
Epoch 0/5... Discriminator Loss: 0.8992... Generator Loss: 1.0941
Epoch 0/5... Discriminator Loss: 1.0683... Generator Loss: 0.6895

Epoch 0/5... Discriminator Loss: 0.8280... Generator Loss: 1.3288
Epoch 0/5... Discriminator Loss: 0.7723... Generator Loss: 1.1165
Epoch 0/5... Discriminator Loss: 0.9696... Generator Loss: 1.3173
Epoch 0/5... Discriminator Loss: 0.8111... Generator Loss: 0.9735
Epoch 0/5... Discriminator Loss: 1.0467... Generator Loss: 0.9259
Epoch 0/5... Discriminator Loss: 0.8300... Generator Loss: 1.0461
Epoch 0/5... Discriminator Loss: 0.9981... Generator Loss: 0.8562
Epoch 0/5... Discriminator Loss: 0.8673... Generator Loss: 1.0135
Epoch 0/5... Discriminator Loss: 0.9547... Generator Loss: 1.0400
Epoch 0/5... Discriminator Loss: 1.2870... Generator Loss: 0.5915

Epoch 0/5... Discriminator Loss: 0.8998... Generator Loss: 1.2411
Epoch 0/5... Discriminator Loss: 0.8902... Generator Loss: 1.1546
Epoch 0/5... Discriminator Loss: 1.1362... Generator Loss: 0.8291
Epoch 0/5... Discriminator Loss: 1.3587... Generator Loss: 0.5966
Epoch 0/5... Discriminator Loss: 1.2987... Generator Loss: 0.6501
Epoch 0/5... Discriminator Loss: 0.9204... Generator Loss: 1.2828
Epoch 0/5... Discriminator Loss: 1.0251... Generator Loss: 0.8541
Epoch 0/5... Discriminator Loss: 0.9665... Generator Loss: 0.8185
Epoch 0/5... Discriminator Loss: 1.0808... Generator Loss: 1.0171
Epoch 0/5... Discriminator Loss: 1.2713... Generator Loss: 0.7443

Epoch 0/5... Discriminator Loss: 1.4917... Generator Loss: 0.6553
Epoch 0/5... Discriminator Loss: 1.1453... Generator Loss: 0.9902
Epoch 0/5... Discriminator Loss: 1.1672... Generator Loss: 0.9469
Epoch 0/5... Discriminator Loss: 1.1246... Generator Loss: 1.0633
Epoch 0/5... Discriminator Loss: 1.0122... Generator Loss: 1.0902
Epoch 0/5... Discriminator Loss: 0.9107... Generator Loss: 1.0391
Epoch 0/5... Discriminator Loss: 1.2973... Generator Loss: 0.8811
Epoch 0/5... Discriminator Loss: 1.1174... Generator Loss: 1.0947
Epoch 0/5... Discriminator Loss: 0.9663... Generator Loss: 1.1997
Epoch 0/5... Discriminator Loss: 1.0260... Generator Loss: 1.1081

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.